Hair pattern determination and filtering

ABSTRACT

Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

BACKGROUND

The amount of accessible content is ever expanding. For example, thereare many online services that host and maintain content for their usersand subscribers. With the sheer volume of accessible content, it can bedifficult for users to find and access relevant content. For example,identifying the proper keywords or queries to obtain relevant contentcan be difficult. Further, browsing content returned in response to aquery to identify relevant content within search results can also betime consuming and difficult.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary computing environment,according to exemplary embodiments of the present disclosure.

FIG. 2 is a flow diagram of an exemplary hair pattern determinationprocess, according to exemplary embodiments of the present disclosure.

FIGS. 3A-3G are illustrations of exemplary user interfaces, according toexemplary embodiments of the present disclosure.

FIG. 4A is a flow diagram of an exemplary filtering process, accordingto exemplary embodiments of the present disclosure.

FIG. 4B is a flow diagram of an exemplary filtering process, accordingto exemplary embodiments of the present disclosure.

FIG. 5 is a flow diagram of an exemplary deep neural network trainingprocess, according to exemplary embodiments of the present disclosure.

FIG. 6 is an illustration of an exemplary client device, according toexemplary embodiments of the present disclosure.

FIG. 7 is an illustration of an exemplary configuration of a clientdevice, such as that illustrated in FIG. 6 , according to exemplaryembodiments of the present disclosure.

FIG. 8 is an illustration of an exemplary server system, according toexemplary embodiments of the present disclosure.

DETAILED DESCRIPTION

As is set forth in greater detail below, embodiments of the presentdisclosure are generally directed to systems and methods for determiningone or more hair patterns presented in content items. The determinedhair patterns may be associated with the content items to facilitateindex, filtering, etc. of the content items based on the determined hairpatterns. In exemplary implementations, a corpus of content itemsincluding visual representations of hair patterns may be stored andmaintained. Each content item may be associated with an embedding vectorthat includes a binary representation of the content item. The embeddingvectors associated with each content item can be provided as inputs to atrained machine learning model, which can process the embedding vectorsto determine one or more hair patterns presented in each content item.Advantageously, embodiments of the present disclosure can determine hairpatterns presented in a content item based on an embedding vector thatis representative of the content item (e.g., in its entirety/as a whole)so as to eliminate the need for performing image pre-processing (e.g.,image segmentation, background subtraction, object detection, etc.) inconnection with the content item prior to determination of the hairpattern(s) presented in the content item.

In exemplary implementations, the hair patterns presented in the corpusof content items hosted and maintained by an online service may bedetermined to facilitate searching, filtering, indexing, etc. of thecorpus of content items. For example, after the hair patterns presentedin a corpus of content items has been determined, the determined hairpattern(s) may be associated with each corresponding content item fromthe corpus of content items. The determined and associated hair patternscan be utilized in identifying content items to present to a user of theonline service in response to a query, as a recommendation based on auser history associated with the user, and the like. Accordingly, thedetermined hair pattern(s) associated with each of the corpus of contentitems can be used to facilitate searching, filtering, indexing, etc. ofthe corpus of content items.

Although embodiments of the present disclosure are described primarilywith respect to processing content items, such as digital images, todetermine, filter, index, etc. hair patterns presented in the contentitems, embodiments of the present disclosure can be applicable to anyother features, attributes, characteristics, etc. presented in contentitems, such as, for example, skin tones, and the like.

FIG. 1 is an illustration of an exemplary computing environment 100,according to exemplary embodiments of the present disclosure.

As shown in FIG. 1 , computing environment 100 may include one or moreclient devices 102, 104, and/or 106, also referred to as user devices,for connecting over network 150 with online service 110, which mayexecute on a network computing system. Online service 110 may includeand/or communicate with one or more data store(s) 112, which may beconfigured to store and maintain a corpus of content items 114. Contentitems 114 may include digital images, videos, etc. and may include avisual representation of a hair pattern (e.g., protective—tight braids,dreadlocks, cornrows, etc.—coily, curly, wavy, straight, shaved/bald,and the like) and may be associated with an embedding vector thatincludes a binary representation of each corresponding content item 114.Online service 110 may form at least a portion of a social mediaplatform or environment, a networking platform or environment, ane-commerce platform or environment, or any other form of interactivecomputing.

Client devices 102, 104, 106 and/or online service 110 may communicatevia wired and/or wireless connections to network 150. Client devices102, 104, and/or 106 may include any type of computing device, such as asmartphone, tablet, laptop computer, desktop computer, wearable, etc.,and network 150 may include any wired or wireless network (e.g., theInternet, cellular, satellite, Bluetooth, Wi-Fi, etc.) that canfacilitate communications between client devices 102. 104, and/or 106and online service 110.

In exemplary implementations, online service 110 may include one or moredeep neural networks (“DNN”), or other machine learning models, thathave been trained to determine one or more hair patterns represented ineach of content items 114. According to exemplary embodiments of thepresent disclosure, the embedding vector associated with each contentitem 114 may be processed by the trained DNN to determine one or morehair patterns presented in each corresponding content item 114.Preferably, the embedding vectors associated with each content item 114include a binary representation of each corresponding content item 114such that the DNN is trained to determine hair patterns in content items114 without performing and pre-processing (e.g., object detection,background subtraction, image segmentation, or other imaging processing)of content items 114.

After the hair patterns have been determined for content items 114, thehair pattern determined for each content item 114 may be associated andstored with each corresponding content item 114 in data store 112. Thedetermined hair pattern(s) associated with each content item 114 can beused to facilitate searching, filtering, indexing, etc. content items114. According to certain aspects, the determined hair patternassociated with each content item 114 may be utilized in the event thatany of content items 114 are used as part of a training dataset for amachine learning system to ensure that the training dataset represents adiverse dataset with respect to hair patterns presented in the contentitems of the training dataset. Additionally, the determined hair patternassociated with each content item 114 can also be used in connectionwith one or more recommendation systems configured to recommend contentitems to a user (e.g., associated with client devices 102, 104, and/or106).

As illustrated in FIG. 1 , users associated with client devices 102,104, and/or 106 may access online service via network 150. In exemplaryimplementations, users associated with client devices 102, 104, and/or106 may access online service 110 to search for, access, etc. content,such as content items 114. In one exemplary implementation, a userassociated with one of client devices 102, 104, and/or 106 may submit aquery (e.g., a text-based query, an image query, etc.) in connectionwith a search for relevant content items. In addition to identifyingcontent items relevant to the query submitted by the user, online system110 may also process the query to determine whether the submitted querymay trigger filtering of the identified content items based on hairpatterns associated with content items 114. For example, the query maybe analyzed to determine if the query has relevance to filtering by hairpattern (e.g., queries related to fashion, beauty, hairstyles, makeup,and the like), if the query is sufficiently generic to allow filteringby hair pattern (e.g., the query does not include keywords directed to aspecific hair pattern, etc.), and the like. Accordingly, a queryrelating to cars or airplanes would likely not trigger filtering by hairpattern, whereas queries relating to hair styles (e.g., weddinghairstyles, prom hairstyles, etc.), fashion, looks worn by celebritieson the red carpet of an event, makeup looks, and the like may triggerfiltering by hair pattern. Consequently, queries that may specify acertain hair pattern (e.g., protective, coily, curly, wavy, straight,bald/shaved, etc.) may also not trigger filtering by hair pattern.According to certain aspects of the present disclosure, determination ofwhether a query triggers filtering by hair pattern may be differentbased on geography, location, etc.

In exemplary implementations where content items responsive to a queryhave been identified and it has been determined that the query triggersfiltering based on hair pattern, an inventory of the responsive contentitems in each hair pattern category may be determined. The inventory foreach hair pattern category may be used to determine whether to enablefiltering based on hair pattern and/or the hair pattern categories thatmay be made available for filtering. For example, if the determinedinventory indicates that the responsive content items only include hairpatterns categorized as straight, filtering based on hair pattern maynot be made available since only one type of hair pattern is presentedin the responsive content items. Similarly, if the determined inventoryindicates that sufficient inventory exists for hair pattern types curly,wavy, protected, and straight, filtering based on hair pattern may beenabled and made available for hair pattern categories curly, wavy,protective, and straight, while filtering based on hair patterncategories coily and bald/shaved may not be made available. Accordingly,the determined inventory for each hair pattern type and/or category maybe compared against a threshold to determine whether sufficientinventory exists for two or more hair pattern types and/or categories toenable and/or make filtering based on hair pattern available and/ordetermining which hair pattern types and/or categories to make availablefor filtering. In exemplary implementations, if it is determined thatsufficient inventory exists for two or more hair patterntypes/categories, filtering based on hair pattern may be enabled for thehair pattern types/categories for which sufficient inventory exists. Thethreshold value may include a predetermined value, a ratio or relativevalue based on the total number of responsive content items and/or theinventory for each hair pattern type/category, and the like.

In other exemplary embodiments, online service 110 may store andmaintain queries that may trigger filtering of responsive content itemsbased on hair pattern. For example, online service 110 may identifyqueries that may trigger filtering based on hair pattern based on therelevance of the queries to hair patterns (e.g., queries related tofashion, beauty, hairstyles, makeup, and the like, as well as whetherthe query is sufficiently generic to allow filtering by hairpattern—e.g., the query does not include keywords directed to a specifichair pattern, etc.) and whether the queries include sufficient inventoryof responsive content items associated with at least one of the hairpattern categories so as to facilitate filtering based on hair pattern.Accordingly, the identified queries may be used to generate, store, andmaintain a corpus of queries that may trigger filtering based on hairpattern, which may be periodically updated (e.g., as additional contentitems become available, etc.). Alternatively, if it is determined that acertain query is not relevant to filtering based on hair pattern and/ordoes not include sufficient inventory for one or more of the hairpattern categories, then it may be determined that the query in questiondoes not trigger filtering based on hair pattern and may be excludedfrom the corpus of queries.

Accordingly, as queries are received from client devices 102, 104,and/or 106, online service 110 may process the received query todetermine whether the received query is included in the corpus ofmaintained queries. If the received query is included in the corpus ofmaintained queries, filtering based on hair pattern may be triggered,whereas if the received query is not included in the corpus ofmaintained queries, filtering based on hair pattern may not betriggered. Additionally, in connection with received queries that arenot included in the corpus of maintained queries such that filteringbased on hair pattern is not triggered, online service 110 may presentone or more recommended queries (e.g., as an autocomplete suggestion,etc.) from the corpus of maintained queries that may trigger filteringbased on hair pattern.

In exemplary implementations where it is determined that a querysubmitted by a user associated with client device 102, 104, and/or 106triggers filtering by hair pattern and sufficient inventory exists toenable filtering based on hair pattern, online service 110 may cause auser interface to be presented on a display of client device 102, 104,and/or 106 to facilitate filtering of the responsive content items basedon hair pattern type. For example, the user interface may present thecontent items responsive to the query and a hair pattern filteringcontrol, which can facilitate filtering of the responsive content itemsby hair pattern type/category. Accordingly, a user may interact with thehair pattern filtering control via client device 102, 104, and/or 106 toselect and/or deselect one or more hair pattern types/categories tofilter the responsive content based on the selected hair patterntype(s). In response to the interaction with the hair pattern filteringcontrol to select one or more of the hair pattern types/categories, theuser interface may be modified to only display the content itemsincluding the selected hair pattern types/categories. The user interfacefacilitating filtering based on hair pattern is described in furtherdetail herein in connection with FIGS. 3A-3G.

FIG. 2 is a flow diagram of an exemplary hair pattern determinationprocess 200, according to exemplary embodiments of the presentdisclosure.

As shown in FIG. 2 , process 200 may begin at step 202 by training amachine learning model which may be trained to receive an embeddingvector associated with a content item and determine one or more hairpatterns presented in the content item. According to exemplaryembodiments, the trained machine learning model may be configured toclassify the hair patterns presented in the content items into varioushair pattern types/categories, such as protective, coily, curly, wavy,straight, bald/shaved, and the like. Determination of bald/shaved as ahair pattern can be advantageous over existing systems in that thedetermination of the lack of hair can provide a more complete anddetailed indication of the dataset of content items in connection withthe determined hair patterns. Training of the machine learning model isdescribed in further detail herein in connection with FIG. 5 .

In step 204, a corpus of content items may be obtained. The contentitems may include digital images, videos, etc. and may include a visualrepresentation of one or more hair patterns. According to certainimplementations, the corpus of content items may have been obtained byfiltering a larger corpus of content items to obtain only content itemsthat may include visual representations of one or more people having oneor more of the hair pattern types/categories. For example, attributes,parameters, metadata, etc. associated with the content items may beanalyzed to discard irrelevant content items that likely do not includevisual representations of a hair pattern, so that only content itemslikely to include a visual representation of one or more hair patternsforms the corpus of content items obtained in step 204.

In step 206, an embedding vector representative of the content item maybe generated and associated with the content item. According to aspectsof the present disclosure, the embedding vector may be representative ofthe content item as a whole (e.g., not segments or portions of thecontent item). As those skilled in the art will appreciate, an“embedding vector” is an array of values that reflect aspects andfeatures of source/input content. For example, an embedding vectorrepresentative of a content item may include an array of valuesdescribing aspects and features of the content item. A process, referredto as an embedding vector generator, that generates an embedding vectorfor input content uses the same learned features to identify and extractinformation, the results of which leads to the generation of theembedding vector. By way of illustration and not limitation, anembedding vector may comprise 128 elements, each element represented bya 32- or 64-bit floating point value, each value representative of someaspect (or multiple aspects) of the input content. In otherimplementations, the embedding vector may have additional or fewerelements and each element may have additional or fewer floating-pointvalues, integer values, and/or binary values. According to exemplaryimplementations of the present disclosure, the generated embeddingvector may be represented as a binary representation of the embeddingvector. For example, the binary representation may be generated usingone or more locality-sensitive hashing (“LSH”) techniques, such as arandom projection method, to generate the binary representation of theembedding vector. According to certain exemplary implementations, thebinary implementation can include 512 bits, 1024 bits, 2048 bits, or anyother number of bits.

Regarding embedding vector generators, typically an embedding vectorgenerator accepts input content (e.g., an image, video, or multi-itemcontent), processes the input content through various layers ofconvolution, and produces an array of values that specifically reflecton the input data, i.e., an embedding vector. Due to the nature of atrained embedding vector generator (i.e., the convolutions that includetransformations, aggregations, subtractions, extrapolations,normalizations, etc.), the contents or values of the resulting embeddingvectors are often meaningless to a personal examination. However,collectively the elements of an embedding vector can be used to projector map the corresponding input content into an embedding space asdefined by the embedding vectors.

The embedding vector associated with the content item can then beprocessed by the trained machine learning model to determine one or morehair patterns presented in the content item, as in step 208. Preferably,the embedding vectors associated with each content item arerepresentative of the content item and the DNN is trained so that hairpatterns can be determined in the content items without performing andpre-processing (e.g., object detection, background subtraction, imagesegmentation, or other imaging processing) of the content items prior todetermining the hair patterns presented in the content items.

In exemplary implementations, the hair patterns determined for eachcontent item may be classified as one of hair pattern type/categoryprotective, coily, curvy, wavy, straight, and bald/shaved. Alternativelyand/or in addition, additional hair pattern types/categories may also beused. Further, where the content item may present more than one hairpattern (e.g., more than one person is presented in the content itemwith different hair patterns, etc.), the trained machine learning modelmay determine the most dominant and/or prominent hair pattern presentedin the content item (e.g., the hair pattern of the main focus of thecontent item while ignoring hair patterns shown in the background),and/or may determine all the hair patterns presented in the contentitem.

After the hair pattern has been determined, the determined hairpattern(s) may be associated with the content item, as in step 210. Forexample, the determined one or more hair patterns may be associated withthe content item as an attribute, a parameter, or other metadataassociated with the content item. In exemplary implementations wheremore than one hair pattern is presented in the content item, only thedominant hair pattern may be associated with the content. Alternativelyand/or in addition, all the determined hair patterns may be associatedwith the content item. According to certain aspects, a prominence scoreof the primary hair pattern may be determined, and if the prominencescore exceeds a threshold value, only the prominent hair pattern may beassociated with the content item, and if the prominence score is below athreshold value, all the determined hair patterns may be associated withthe content item.

In step 212, it may be determined if there is another content item inthe corpus of content items for processing. If additional content itemsremain, process 200 returns to step 206 to process the next contentitem. If no further content items remain, process 200 may complete.

FIGS. 3A-3G are illustrations of exemplary user interfaces, according toexemplary embodiments of the present disclosure.

As shown in FIG. 3A, user interface 300 may present hair pattern filtercontrol 302 and content items 304-1, 304-2, 304-3, 304-4, 304-5, and/or304-6. In exemplary implementations, user interface 300 may be presentedon a display of a client device (e.g., client device 102, 104, and/or106) in response to a query submitted by a user associated with theclient device. For example, user interface 300 may be presented on adisplay of a client device in response to a query after it has beendetermined that the query submitted by the user triggers filtering basedon hair pattern and that sufficient inventory exists for each of thehair pattern filter options included in hair pattern filter control 302.

Content items 304-1, 304-2, 304-3, 304-4, 304-5, and/or 304-6 may havebeen identified (e.g., from a corpus of content items such as contentitems 114) as content items that are responsive to the query, and thequery may have been analyzed to determine if the query has relevance tofiltering by hair pattern (e.g., queries related to fashion, beauty,hairstyles, makeup, and the like), if the query is sufficiently genericto allow filtering by hair pattern (e.g., the query does not includekeywords directed to a specific hair pattern, etc.), and the like. Afterit has been determined that the query triggers filtering based on hairpattern, an inventory of the responsive content items in each hairpattern category may be determined. For example, in the corpus ofresponsive content items, the inventory (e.g., the number, aproportional/relative number, etc.) of content items associated witheach hair pattern type/category may be determined. The inventory may beanalyzed to determine whether sufficient inventory for each hair patterntype/category exists to enable filtering based on hair pattern andpresentation of each corresponding hair pattern type/category as anoption in hair pattern filter control 302. In the exemplaryimplementation illustrated in FIG. 3A, it may have been determined thatthe query triggers filtering based on hair pattern and that sufficientinventory exists for each of the hair pattern type/category options(e.g., protective, coily, curly, wavy, straight, and bald/shaved) sothat each hair pattern type/category option is presented in hair patternfilter control 302.

Further, content items 304-1, 304-2, 304-3, 304-4, 304-5, and/or 304-6may have been selected and arranged in the presentation shown in FIG. 3Ato ensure that the user is presented with a diverse set of contentitems. For example, content items 304-1, 304-2, 304-3, 304-4, 304-5,and/or 304-6, which may have been identified as being relevant and/orresponsive to a query, may each include a corresponding ranking (e.g.,based on relevance), which may also include a diversification componentbased on one or more attributes such as, for example, hair pattern, skintone, gender, age, geographic location, or any other attributesassociated with the content items to ensure that a diverse set ofcontent items are presented to the user. The diversification componentcan be determined using diversification heuristics, a maximal marginalrelevance (MMR) approach, a determinantal point processes (DPP), othertrained machine learning models and/or probabilistic models, or otheralgorithms or techniques. Further, the diversification component may bedetermined in batch. Accordingly, content items 304-1, 304-2, 304-3,304-4, 304-5, and/or 304-6 may be selected, sorted, arranged, and/orpresented based on diversity, in addition to relevance andresponsiveness to the query, such that the presented content items arediverse, as well as relevant and responsive to the query.

In the exemplary implementation illustrated in FIG. 3A, hair patternfilter control 302 may include one or more hair pattern type/categoryoptions (e.g., protective, coily, curly, wavy, straight, andbald/shaved) based on which the user may choose to filter the responsivecontent items, and the user may interact with hair pattern filtercontrol 302 to select one or more of the hair pattern type/categoryoptions (e.g., protective, coily, curly, wavy, straight, andbald/shaved) to filter the responsive content items to only display thecontent items associated with the selected hair pattern type/categoryoptions. Hair pattern filter control 302 may also include an interactivefeature (e.g., shown as an “i” in a circle, etc.) with which a user mayinteract (e.g., select, hover over, click, etc.) to obtain furtherinformation, descriptions, and the like for each hair patterntype/category. Accordingly, in response to a selection of one or morehair pattern type/category options presented by hair pattern filtercontrol 302, content items 304-1, 304-2, 304-3, 304-4, 304-5, and/or304-6 may be filtered to present only the content items associated withthe selected hair pattern type/category option(s). Accordingly, the usermay then browse, access, or otherwise consume the curated content itemswhich have been filtered based on the selected hair patterntype/category options.

FIGS. 3B-3G illustrate exemplary user interfaces, according to exemplaryembodiments of the present disclosure. Although the exemplary userinterfaces shown in FIGS. 3B-3G illustrate implementations where onlyone hair pattern type/category is selected, according to certain aspectsof the present disclosure, more than one hair pattern type/category maybe selected and the content items presented may be filtered based on allthe selected hair pattern types/categories.

The user interfaces shown in FIGS. 3B-3G may present a hair patternfilter control and one or more content items to the user, and may bepresented to the user subsequent to presentation of user interface 300,as shown in FIG. 3A, after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 302

As shown in FIG. 3B, user interface 310 may include hair pattern filtercontrol 312 and content items 314-1, 314-2, 314-3, 314-4, 314-5, and314-6. User interface 310 illustrated in FIG. 3B may be presented to theuser subsequent to presentation of user interface 300 shown in FIG. 3A(or any of FIGS. 3C-3G) after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 312.

For example, as shown in FIG. 3B, the PROTECTIVE hair patterntype/category option is highlighted in hair pattern filter control 312,indicating that the user has selected the PROTECTIVE hair patterntype/category option via an interaction with hair pattern filter control312. Accordingly, the content items that were identified as beingresponsive to the query are filtered by the user's choice of thePROTECTIVE hair pattern type/category option, and only the content itemsassociated with the PROTECTIVE hair pattern type/category may bepresented via user interface 310 to the user. Alternatively and/or inaddition, in implementations where the responsive content items do notinclude a sufficient number of content items associated with thePROTECTIVE hair pattern type/category, additional responsive contentitems associated with the PROTECTIVE hair pattern type/category may alsobe identified (e.g., from the corpus of content items such as contentitems 114). Thus, content items 314-1, 314-2, 314-3, 314-4, 314-5, and314-6, which are presented to the user via user interface 310, may allbe associated with the PROTECTIVE hair pattern type/category. Further,the filtered content items, i.e., content items 314-1, 314-2, 314-3,314-4, 314-5, and 314-6, may have been selected and arranged in thepresentation shown in FIG. 3B to ensure that the user is presented witha diverse set of content items based on one or more attributes such as,for example, hair pattern, skin tone, gender, age, geographic location,or any other attributes associated with the content items to ensure thata diverse set of content items are presented to the user. Thediversification of the content items may have been determined using oneor more of diversification heuristics, a maximal marginal relevance(MMR) approach, a determinantal point processes (DPP), other trainedmachine learning models and/or probabilistic models, or other algorithmsor techniques. Accordingly, content items 314-1, 314-2, 314-3, 314-4,314-5, and 314-6 may be selected, sorted, arranged, and/or presentedbased on diversity, in addition to filtering, relevance, and/orresponsiveness to the query,

As shown in FIG. 3C, user interface 320 may include hair pattern filtercontrol 322 and content items 324-1, 324-2, 324-3, 324-4, 324-5, and324-6. User interface 320 illustrated in FIG. 3C may be presented to theuser subsequent to presentation of user interface 300 shown in FIG. 3A(or any of FIG. 3B or 3D-3G) after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 322.

For example, as shown in FIG. 3C, the COILY hair pattern type/categoryoption is highlighted in hair pattern filter control 322, indicatingthat the user has selected the COILY hair pattern type/category optionvia an interaction with hair pattern filter control 322. Accordingly,the content items that were identified as being responsive to the queryare filtered by the user's choice of the COILY hair patterntype/category option, and only the content items associated with theCOILY hair pattern type/category may be presented via user interface 320to the user. Alternatively and/or in addition, in implementations wherethe responsive content items do not include a sufficient number ofcontent items associated with the COILY hair pattern type/category,additional responsive content items associated with the COILY hairpattern type/category may also be identified (e.g., from the corpus ofcontent items such as content items 114). Thus, content items 324-1,324-2, 324-3, 324-4, 324-5, and 324-6, which are presented to the uservia user interface 320, may all be associated with the COILY hairpattern type/category. Further, the filtered content items, i.e.,content items 324-1, 324-2, 324-3, 324-4, 324-5, and 324-6, may havebeen selected and arranged in the presentation shown in FIG. 3C toensure that the user is presented with a diverse set of content itemsbased on one or more attributes such as, for example, hair pattern, skintone, gender, age, geographic location, or any other attributesassociated with the content items to ensure that a diverse set ofcontent items are presented to the user. The diversification of thecontent items may have been determined using or more of diversificationheuristics, a maximal marginal relevance (MMR) approach, a determinantalpoint processes (DPP), other trained machine learning models and/orprobabilistic models, or other algorithms or techniques. Accordingly,content items 324-1, 324-2, 324-3, 324-4, 324-5, and 324-6 may beselected, sorted, arranged, and/or presented based on diversity, inaddition to filtering, relevance, and/or responsiveness to the query,

As shown in FIG. 3D, user interface 330 may include hair pattern filtercontrol 332 and content items 334-1, 334-2, 334-3, 334-4, 334-5, and334-6. User interface 330 illustrated in FIG. 3D may be presented to theuser subsequent to presentation of user interface 300 shown in FIG. 3A(or any of FIG. 3B, 3C, or 3E-3G) after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 332.

For example, as shown in FIG. 3D, the CURLY hair pattern type/categoryoption is highlighted in hair pattern filter control 332, indicatingthat the user has selected the CURLY hair pattern type/category optionvia an interaction with hair pattern filter control 332. Accordingly,the content items that were identified as being responsive to the queryare filtered by the user's choice of the CURLY hair patterntype/category option, and only the content items associated with theCURLY hair pattern type/category may be presented via user interface 330to the user. Alternatively and/or in addition, in implementations wherethe responsive content items do not include a sufficient number ofcontent items associated with the CURLY hair pattern type/category,additional responsive content items associated with the CURLY hairpattern type/category may also be identified (e.g., from the corpus ofcontent items such as content items 114). Thus, content items 334-1,334-2, 334-3, 334-4, 334-5, and 334-6, which are presented to the uservia user interface 330, may all be associated with the CURLY hairpattern type/category. Further, the filtered content items, i.e.,content items 334-1, 334-2, 334-3, 334-4, 334-5, and 334-6, may havebeen selected and arranged in the presentation shown in FIG. 3D toensure that the user is presented with a diverse set of content itemsbased on one or more attributes such as, for example, hair pattern, skintone, gender, age, geographic location, or any other attributesassociated with the content items to ensure that a diverse set ofcontent items are presented to the user. The diversification of thecontent items may have been determined using or more of diversificationheuristics, a maximal marginal relevance (MMR) approach, a determinantalpoint processes (DPP), other trained machine learning models and/orprobabilistic models, or other algorithms or techniques. Accordingly,content items 334-1, 334-2, 334-3, 334-4, 334-5, and 334-6 may beselected, sorted, arranged, and/or presented based on diversity, inaddition to filtering, relevance, and/or responsiveness to the query,

As shown in FIG. 3E, user interface 340 may include hair pattern filtercontrol 342 and content items 344-1, 344-2, 344-3, 344-4, 344-5, and344-6. User interface 340 illustrated in FIG. 3E may be presented to theuser subsequent to presentation of user interface 300 shown in FIG. 3A(or any of FIG. 3B-3D, 3F, or 3G) after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 342.

For example, as shown in FIG. 3E, the WAVY hair pattern type/categoryoption is highlighted in hair pattern filter control 342, indicatingthat the user has selected the WAVY hair pattern type/category optionvia an interaction with hair pattern filter control 342. Accordingly,the content items that were identified as being responsive to the queryare filtered by the user's choice of the WAVY hair pattern type/categoryoption, and only the content items associated with the WAVY hair patterntype/category may be presented via user interface 340 to the user.Alternatively and/or in addition, in implementations where theresponsive content items do not include a sufficient number of contentitems associated with the WAVY hair pattern type/category, additionalresponsive content items associated with the WAVY hair patterntype/category may also be identified (e.g., from the corpus of contentitems such as content items 114). Thus, content items 344-1, 344-2,344-3, 344-4, 344-5, and 344-6, which are presented to the user via userinterface 340, may all be associated with the WAVY hair patterntype/category. Further, the filtered content items, i.e., content items344-1, 344-2, 344-3, 344-4, 344-5, and 344-6, may have been selected andarranged in the presentation shown in FIG. 3E to ensure that the user ispresented with a diverse set of content items based on one or moreattributes such as, for example, hair pattern, skin tone, gender, age,geographic location, or any other attributes associated with the contentitems to ensure that a diverse set of content items are presented to theuser. The diversification of the content items may have been determinedusing or more of diversification heuristics, a maximal marginalrelevance (MMR) approach, a determinantal point processes (DPP), othertrained machine learning models and/or probabilistic models, or otheralgorithms or techniques. Accordingly, content items 344-1, 344-2,344-3, 344-4, 344-5, and 344-6 may be selected, sorted, arranged, and/orpresented based on diversity, in addition to filtering, relevance,and/or responsiveness to the query,

As shown in FIG. 3F, user interface 350 may include hair pattern filtercontrol 352 and content items 354-1, 354-2, 354-3, 354-4, 354-5, and354-6. User interface 350 illustrated in FIG. 3F may be presented to theuser subsequent to presentation of user interface 300 shown in FIG. 3A(or any of FIG. 3B-3E or 3G) after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 352.

For example, as shown in FIG. 3F, the STRAIGHT hair patterntype/category option is highlighted in hair pattern filter control 352,indicating that the user has selected the STRAIGHT hair patterntype/category option via an interaction with hair pattern filter control352. Accordingly, the content items that were identified as beingresponsive to the query are filtered by the user's choice of theSTRAIGHT hair pattern type/category option, and only the content itemsassociated with the STRAIGHT hair pattern type/category may be presentedvia user interface 350 to the user. Alternatively and/or in addition, inimplementations where the responsive content items do not include asufficient number of content items associated with the STRAIGHT hairpattern type/category, additional responsive content items associatedwith the STRAIGHT hair pattern type/category may also be identified(e.g., from the corpus of content items such as content items 114).Thus, content items 354-1, 354-2, 354-3, 354-4, 354-5, and 354-6, whichare presented to the user via user interface 350, may all be associatedwith the STRAIGHT hair pattern type/category. Further, the filteredcontent items, i.e., content items 354-1, 354-2, 354-3, 354-4, 354-5,and 354-6, may have been selected and arranged in the presentation shownin FIG. 3F to ensure that the user is presented with a diverse set ofcontent items based on one or more attributes such as, for example, hairpattern, skin tone, gender, age, geographic location, or any otherattributes associated with the content items to ensure that a diverseset of content items are presented to the user. The diversification ofthe content items may have been determined using or more ofdiversification heuristics, a maximal marginal relevance (MMR) approach,a determinantal point processes (DPP), other trained machine learningmodels and/or probabilistic models, or other algorithms or techniques.Accordingly, content items 354-1, 354-2, 354-3, 354-4, 354-5, and 354-6may be selected, sorted, arranged, and/or presented based on diversity,in addition to filtering, relevance, and/or responsiveness to the query,

As shown in FIG. 3G, user interface 360 may include hair pattern filtercontrol 362 and content items 364-1, 364-2, 364-3, 364-4, 364-5, and364-6. User interface 360 illustrated in FIG. 3G may be presented to theuser subsequent to presentation of user interface 300 shown in FIG. 3A(or any of FIGS. 3B-3F) after the user has made a hair patterntype/category option selection via an interaction with hair patternfilter control 362.

For example, as shown in FIG. 3G, the BALD/SHAVED hair patterntype/category option is highlighted in hair pattern filter control 362,indicating that the user has selected the BALD/SHAVED hair patterntype/category option via an interaction with hair pattern filter control362. Accordingly, the content items that were identified as beingresponsive to the query are filtered by the user's choice of theBALD/SHAVED hair pattern type/category option, and only the contentitems associated with the BALD/SHAVED hair pattern type/category may bepresented via user interface 360 to the user. Alternatively and/or inaddition, in implementations where the responsive content items do notinclude a sufficient number of content items associated with theBALD/SHAVED hair pattern type/category, additional responsive contentitems associated with the BALD/SHAVED hair pattern type/category mayalso be identified (e.g., from the corpus of content items such ascontent items 114). Thus, content items 364-1, 364-2, 364-3, 364-4,364-5, and 364-6, which are presented to the user via user interface360, may all be associated with the BALD/SHAVED hair patterntype/category. Further, the filtered content items, i.e., content items364-1, 364-2, 364-3, 364-4, 364-5, and 364-6, may have been selected andarranged in the presentation shown in FIG. 3G to ensure that the user ispresented with a diverse set of content items based on one or moreattributes such as, for example, hair pattern, skin tone, gender, age,geographic location, or any other attributes associated with the contentitems to ensure that a diverse set of content items are presented to theuser. The diversification of the content items may have been determinedusing or more of diversification heuristics, a maximal marginalrelevance (MMR) approach, a determinantal point processes (DPP), othertrained machine learning models and/or probabilistic models, or otheralgorithms or techniques. Accordingly, content items 364-1, 364-2,364-3, 364-4, 364-5, and 364-6 may be selected, sorted, arranged, and/orpresented based on diversity, in addition to filtering, relevance,and/or responsiveness to the query,

FIG. 4A is a flow diagram of an exemplary filtering process, accordingto exemplary embodiments of the present disclosure.

As shown in FIG. 4A, process 400 may begin at step 402, where a querymay be obtained from a user. For example, a user may submit a query viaa client device (e.g., client devices 102, 104, and/or 106) inconnection with a search for content items. In step 404, content itemsresponsive to the query may be obtained. For example, an online service(e.g., online service 110) may identify content items from a corpus ofcontent items (e.g., content items 114) that may be responsive and/orrelevant to the query. The relevant content items may be identified, forexample, based on embedding vectors, attributes, metadata, etc.associated with the content items and the query, and may utilize variousclustering algorithms, similarity metrics, and the like. According tocertain aspects of the present disclosure, the identified content itemsmay be ranked according to relevance to the query.

Additionally, the query may be processed to determine whether thesubmitted query may trigger filtering of the identified content itemsbased on hair pattern, as in step. 406. For example, the query may beanalyzed to determine if the query has relevance to filtering by hairpattern (e.g., queries related to fashion, beauty, hairstyles, makeup,and the like), if the query is sufficiently generic to allow filteringby hair pattern (e.g., the query does not include keywords directed to aspecific hair pattern, etc.), and the like.

In the event that it is determined that the query does not triggerfiltering based on hair pattern, the content items identified in step404 as being relevant and/or responsive to the query may be presented tothe user, as in step 412. Accordingly, a query unrelated to hairpatterns and/or likely to identify responsive content items that do notinclude representations of hair patterns may not trigger filtering basedon hair pattern.

If it has been determined that the query triggers filtering based onhair pattern, an inventory of the responsive content items in each hairpattern type/category may be determined, as in step 408. For example,the number of content items in the responsive content items identifiedin step 404 that are associated with each hair pattern type/category maybe determined. This can include an absolute number, a relative number(e.g., to the inventory of each hair pattern type/category), aproportional number (e.g., relative to the total number of responsivecontent items identified in step 404), etc. The inventory for each hairpattern category may be used to determine whether to enable filteringbased on hair pattern and/or the hair pattern categories that may bemade available for filtering. For example, if the determined inventoryindicates that the responsive content items only include a single typeof hair pattern type/category (e.g., one of protective, coily, curly,wavy, straight, or bald/shaved), filtering based on hair pattern may notbe made available since only one type of hair pattern is included in theresponsive content items. In such a scenario, the content itemsidentified in step 404 as being relevant and/or responsive to the querymay be presented to the user, as in step 412.

In the event that sufficient inventory exists for at least two hairpattern types/categories, as in step 410, filtering based on hairpattern may be enabled and made available for hair pattern categoriesfor which sufficient inventory exists. For example, if it is determinedthat sufficient inventory exists for protective, coily, and wavy,filtering based on protective, coily, wavy made be made available, whilefiltering based on curvy, straight, and bald/shaved may not be madeavailable. According to exemplary implementations, the determinedinventory for each hair pattern type and/or category may be comparedagainst a threshold to determine whether sufficient inventory exists foreach hair pattern type/category. The threshold value may include apredetermined value, a ratio or relative value based on the total numberof responsive content items and/or the inventory for each hair patterntype/category, and the like.

After it has been determined that sufficient inventory exists to enablefiltering based on at least two of the hair pattern types/categories, instep 414, a filter control may be presented, via a user interface, withthe responsive content items presented to the user. According toexemplary implementations of the present disclosure, the content itemspresented to the user may be selected to ensure presentation of adiverse set of content items based on one or more attributes associatedwith the content items. For example, the ranking of the responsivecontent items identified in step 404 also include a diversificationcomponent based on one or more attributes such as, for example, hairpattern, skin tone, gender, age, geographic location, or any otherattributes associated with the content items to ensure that a diverseset of content items are presented to the user. The diversificationcomponent can be determined using diversification heuristics, a maximalmarginal relevance (MMR) approach, a determinantal point processes(DPP), other trained machine learning models and/or probabilisticmodels, or other algorithms or techniques. Further, the diversificationcomponent may be determined in batch. Accordingly, the identifiedcontent items may be sorted and selected based on diversity, in additionto relevance and responsiveness to the query, and the presented to theuser such that the presented content items are diverse, as well asrelevant and responsive to the query.

In step 416, an interaction with the filter control may be received,indicating a selection of one or more hair pattern types/categories. Forexample, the user may have selected one or more of hair patterntypes/categories protective, coily, curly, wavy, straight, and/orshaved/bald. In response, the presented content items may be filteredbased on the hair pattern type/category selected by the user such thatonly the content items associated with the selected hair patterns may bepresented, as in step 418. In an exemplary implementation where the userinteracted with the hair pattern filter control to select the coily hairpattern type/category, only the content items associated with the coilyhair pattern type/category may be presented to the user. Similarly, inan exemplary implementation where the user has selected the wavy andstraight hair pattern types/categories via an interaction with the hairpattern filter control, only the content items associated with the wavyand straight hair pattern types/categories may be presented to the user.Additionally, the filtered content items presented to the user based onthe selected hair pattern type/category may be presented based at leastin part on a diversity ranking associated with the filtered contentitems to ensure that the filtered content items presented to the useralso include a diverse set of content items. Process 400 may be repeatedfor each received query.

FIG. 4B is a flow diagram of an exemplary filtering process, accordingto exemplary embodiments of the present disclosure.

As shown in FIG. 4B, process 450 may begin at step 452, where a corpusof hair pattern filtering queries may be generated and maintained. Forexample, queries may be processed to identify queries that haverelevance to filtering by hair pattern (e.g., queries related tofashion, beauty, hairstyles, makeup, and the like), are sufficientlygeneric to allow filtering by hair pattern (e.g., the query does notinclude keywords directed to a specific hair pattern, etc.), and includesufficient inventory of responsive content items associated with atleast one of the hair pattern categories so as to facilitate filteringbased on hair pattern. According to certain aspects of the presentdisclosure, determination of whether a query triggers filtering by hairpattern may be different based on geography, location, etc. Accordingly,the identified queries may be used to generate, store, and maintain acorpus of queries that may trigger filtering based on hair pattern,which may be periodically updated (e.g., as additional content itemsbecome available, etc.). Alternatively, if it is determined that acertain query is not relevant to filtering based on hair pattern and/ordoes not include sufficient inventory for one or more of the hairpattern categories, then it may be determined that the query in questiondoes not trigger filtering based on hair pattern and may be excludedfrom the corpus of queries.

In step 454, a query may be obtained from a user. For example, a usermay submit a query via a client device (e.g., client devices 102, 104,and/or 106) in connection with a search for content items. The query maybe processed, in step 456, to determine whether the submitted query maytrigger filtering of the identified content items based on hair pattern.For example, the received query may be processed to determine whetherthe received query is included in the corpus of triggering queries. Ifthe received query is included in the corpus of triggering queries,filtering based on hair pattern may be triggered, whereas if thereceived query is not included in the corpus of triggering queries,filtering based on hair pattern may not be triggered. If it isdetermined that the received query is not included in the corpus oftriggering queries such that filtering based on hair pattern is nottriggered, one or more recommended queries (e.g., as an autocompletesuggestion, etc.) from the corpus of triggering queries that may triggerfiltering based on hair pattern may be optionally recommended andpresented, as in step 458.

In step 460, content items relevant and/or responsive to the query maybe identified and presented to the user, along with a filter control.According to exemplary implementations of the present disclosure, thecontent items presented to the user may be selected to ensurepresentation of a diverse set of content items based on one or moreattributes associated with the content items.

In step 462, an interaction with the filter control may be receivedindicating a selection of one or more hair pattern types/categories. Forexample, the user may have selected one or more of hair patterntypes/categories protective, coily, curly, wavy, straight, and/orshaved/bald. In response, the presented content items may be filteredbased on the hair pattern type/category selected by the user such thatonly the content items associated with the selected hair patterns may bepresented, as in step 464. In an exemplary implementation where the userinteracted with the hair pattern filter control to select the coily hairpattern type/category, only the content items associated with the coilyhair pattern type/category may be presented to the user. Similarly, inan exemplary implementation where the user has selected the wavy andstraight hair pattern types/categories via an interaction with the hairpattern filter control, only the content items associated with the wavyand straight hair pattern types/categories may be presented to the user.Additionally, the filtered content items presented to the user based onthe selected hair pattern type/category may be presented based at leastin part on a diversity ranking associated with the filtered contentitems to ensure that the filtered content items presented to the useralso include a diverse set of content items. Process 450 may then returnto step 454 to process a further query.

FIG. 5 is a flow diagram of an exemplary training process 500 fortraining a DNN (or other machine learning model), according to exemplaryembodiments of the present disclosure.

As shown in FIG. 5 , training process 500 is configured to train anuntrained DNN 534 operating on computer system 540 to transformuntrained DNN 534 into trained DNN 536 that operates on the same oranother computer system, such as online service 110. In the course oftraining, as shown in FIG. 5 , at step 502, untrained DNN 534 isinitialized with training criteria 530. Training criteria 530 mayinclude, but is not limited to, information as to a type of training,number of layers to be trained, candidate labels, etc.

At step 504 of training process 500, corpus of labeled training data532, may be accessed. For example, if training is to generate a trainedDNN that predicts hair pattern types/categories, labeled training data532 may include labeled content items presenting the various hairpattern types/categories, and the like. According to certain aspects ofthe present disclosure, labeled training data 532 may include contentitems that are labeled by multiple sources and an aggregation of themultiple labels (e.g., mean, median, mode, etc.) may be used as thelabel for each item of labeled training data 532.

The disclosed implementations discuss the use of labeled training data,meaning that the actual results of processing of the data items of thecorpus of training data (i.e., whether the data corresponds to apositive or negative presence of a condition) are known. Of course, invarious implementations, the training data 532 may also or alternativelyinclude unlabeled training data.

With training data 532 accessed, at step 506, training data 532 isdivided into training and validation sets. Generally speaking, the itemsof data in the training set are used to train untrained DNN 534 and theitems of data in the validation set are used to validate the training ofthe DNN. As those skilled in the art will appreciate, and as describedbelow in regard to much of the remainder of training process 500, thereare numerous iterations of training and validation that occur during thetraining of the DNN.

At step 508 of training process 500, the data items of the training setare processed, often in an iterative manner. Processing the data itemsof the training set includes capturing the processed results. Afterprocessing the items of the training set, at step 510, the aggregatedresults of processing the training set are evaluated, and at step 512, adetermination is made as to whether a desired performance has beenachieved. If the desired performance is not achieved, in step 514,aspects of the machine learning model are updated in an effort to guidethe machine learning model to generate more accurate results, andprocessing returns to step 506, where a new set of training data isselected, and the process repeats. Alternatively, if the desiredperformance is achieved, training process 500 advances to step 516.

At step 516, and much like step 508, the data items of the validationset are processed, and at step 518, the processing performance of thisvalidation set is aggregated and evaluated. At step 520, a determinationis made as to whether a desired performance, in processing thevalidation set, has been achieved. If the desired performance is notachieved, in step 514, aspects of the machine learning model are updatedin an effort to guide the machine learning model to generate moreaccurate results, and processing returns to step 506. Alternatively, ifthe desired performance is achieved, the training process 500 advancesto step 522.

At step 522, a finalized, trained DNN 536 is generated for determininghair pattern types/categories. Typically, though not exclusively, aspart of finalizing the now-trained DNN 536, portions of the DNN that areincluded in the model during training for training purposes areextracted, thereby generating a more efficient trained DNN 536.

FIG. 6 illustrates an exemplary client device 600 that can be used inaccordance with various implementations described herein. In thisexample, client device 600 includes display 602 and optionally, at leastone input component 604, such as a camera, on a same side and/oropposite side of the device as display 602. Client device 600 may alsoinclude an audio transducer, such as speaker 606, and microphone 608.Generally, client device 600 may have any form of input/outputcomponents that allow a user to interact with client device 600. Forexample, the various input components for enabling user interaction withthe device may include touch-based display 602 (e.g., resistive,capacitive, Interpolating Force-Sensitive Resistance (IFSR)), camera(for gesture tracking, etc.), microphone, global positioning system(GPS), compass or any combination thereof. One or more of these inputcomponents may be included on a user device or otherwise incommunication with the user device. Various other input components andcombinations of input components can be used as well within the scope ofthe various implementations, as should be apparent in light of theteachings and suggestions contained herein.

In order to provide the various functionality described herein, FIG. 7illustrates an exemplary configuration 700 of a client device, such asclient device 600 described with respect to FIG. 6 and discussed herein.In this example, the device includes at least one central processor 702for executing instructions that can be stored in at least one memorydevice or element 704. As would be apparent to one of ordinary skill inthe art, the device can include many types of memory, data storage orcomputer-readable storage media, such as a first data storage forprogram instructions for execution by the one or more processors 702.Removable storage memory can be available for sharing information withother devices, etc. The device typically will include some type ofdisplay 706, such as a touch-based display, electronic ink (e-ink),organic light emitting diode (OLED), liquid crystal display (LCD), etc.

As discussed, the device in many implementations will include at leastone image capture element 708, such as one or more cameras that are ableto capture image objects in the vicinity of the device. An image captureelement can include, or be based at least in part upon, any appropriatetechnology, such as a CCD or CMOS image capture element having adetermined resolution, focal range, viewable area, and capture rate. Thedevice can include at least one application component 710 for performingthe implementations discussed herein. Optionally, the device can includetrained DNN 712, which can be configured to determine hair patterntypes/categories according to the implementations described herein. Theuser device may be in constant or intermittent communication with one ormore remote computing resources and may exchange information, such aslivestream feeds, chat messages, etc., with the remote computingsystem(s) as part of the disclosed implementations.

The device also can include at least one location component, such asGPS, NFC location tracking, Wi-Fi location monitoring, etc. The exampleclient device may also include at least one additional input device ableto receive conventional input from a user. This conventional input caninclude, for example, a push button, touch pad, touch-based display,wheel, joystick, keyboard, mouse, trackball, keypad or any other suchdevice or element whereby a user can submit an input to the device.These I/O devices could be connected by a wireless, infrared, Bluetooth,or other link as well in some implementations. In some implementations,however, such a device might not include any buttons at all and might becontrolled only through touch inputs (e.g., touch-based display), audioinputs (e.g., spoken), or a combination thereof.

FIG. 8 is a pictorial diagram of an illustrative implementation of aserver system 800 that may be used with one or more of theimplementations described herein. Server system 800 may include one ormore processors 801, such as one or more redundant processors, videodisplay adapter 802, disk drive 804, input/output interface 806, networkinterface 808, and memory 812. Processor(s) 801, video display adapter802, disk drive 804, input/output interface 806, network interface 808,and memory 812 may be communicatively coupled to each other bycommunication bus 810.

Video display adapter 802 provides display signals to a local displaypermitting an operator of server system 800 to monitor and configureoperation of server system 800. Input/output interface 806 likewisecommunicates with external input/output devices not shown in FIG. 8 ,such as a mouse, keyboard, scanner, or other input and output devicesthat can be operated by an operator of server system 800. Networkinterface 808 includes hardware, software, or any combination thereof,to communicate with other computing devices. For example, networkinterface 808 may be configured to provide communications between serversystem 800 and other computing devices, such as client device 600.

Memory 812 generally comprises random access memory (RAM), read-onlymemory (ROM), flash memory, and/or other volatile or permanent memory.Memory 812 is shown storing operating system 814 for controlling theoperation of server system 800. Server system 800 may also includetrained DNN 816, as discussed herein. In some implementations, trainedDNN 816 may determine hair pattern types/categories according to theimplementations described herein. In other implementations, trained DNN816 may exist on both server system 800 and/or each client device (e.g.,DNN 712).

Memory 812 additionally stores program code and data for providingnetwork services that allow client devices and external sources toexchange information and data files with server system 800. Memory 812may also include interactive trained DNN 816, which may communicate withdata store manager application 818 to facilitate data exchange andmapping between the data store 803, user/client devices, such as clientdevices 102, 104, and/or 106, external sources, etc.

As used herein, the term “data store” refers to any device orcombination of devices capable of storing, accessing and retrievingdata, which may include any combination and number of data servers,databases, data storage devices and data storage media, in any standard,distributed or clustered environment. Remote computing resource 800 caninclude any appropriate hardware and software for integrating with thedata store 803 as needed to execute aspects of one or more applicationsfor the client device 600, the external sources, etc.

Data store 803 can include several separate data tables, databases orother data storage mechanisms and media for storing data relating to aparticular aspect. For example, data store 803 illustrated includesdigital items (e.g., images) and corresponding metadata (e.g., imagesegments, popularity, source) about those items.

It should be understood that there can be many other aspects that may bestored in data store 803, which can be stored in any of the above listedmechanisms as appropriate or in additional mechanisms of any of the datastore. Data store 803 may be operable, through logic associatedtherewith, to receive instructions from server system 800 and obtain,update or otherwise process data in response thereto.

Server system 800, in one implementation, is a distributed environmentutilizing several computer systems and components that areinterconnected via communication links, using one or more computernetworks or direct connections. However, it will be appreciated by thoseof ordinary skill in the art that such a system could operate equallywell in a system having fewer or a greater number of components than areillustrated in FIG. 8 . Thus, the depiction in FIG. 8 should be taken asbeing illustrative in nature and not limiting to the scope of thedisclosure.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storage mediamay be implemented by a volatile computer memory, non-volatile computermemory, hard drive, solid-state memory, flash drive, removable disk,and/or other media. In addition, components of one or more of themodules and engines may be implemented in firmware or hardware.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers, communications, media files, andmachine learning should recognize that components and process stepsdescribed herein may be interchangeable with other components or steps,or combinations of components or steps, and still achieve the benefitsand advantages of the present disclosure. Moreover, it should beapparent to one skilled in the art that the disclosure may be practicedwithout some, or all of the specific details and steps disclosed herein.

Moreover, with respect to the one or more methods or processes of thepresent disclosure shown or described herein, including but not limitedto the flow charts shown in FIGS. 2, 4A, 4B, and 5 , orders in whichsuch methods or processes are presented are not intended to be construedas any limitation on the claims, and any number of the method or processsteps or boxes described herein can be combined in any order and/or inparallel to implement the methods or processes described herein. Inaddition, some process steps or boxes may be optional. Also, thedrawings herein are not drawn to scale.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storage mediamay be implemented by a volatile computer memory, non-volatile computermemory, hard drive, solid-state memory, flash drive, removable disk,and/or other media. In addition, components of one or more of themodules and engines may be implemented in firmware or hardware.

Disjunctive language such as the phrase “at least one of X, Y, or Z,” or“at least one of X, Y and Z,” unless specifically stated otherwise, isotherwise understood with the context as used in general to present thatan item, term, etc., may be any of X, Y, or Z, or any combinationthereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is notgenerally intended to, and should not, imply that certainimplementations require at least one of X, at least one of Y, or atleast one of Z to each be present.

Unless otherwise explicitly stated, articles such as “a” or “an” shouldgenerally be interpreted to include one or more described items.Accordingly, phrases such as “a device configured to” or “a deviceoperable to” are intended to include one or more recited devices. Suchone or more recited devices can also be collectively configured to carryout the stated recitations. For example, “a processor configured tocarry out recitations A, B and C” can include a first processorconfigured to carry out recitation A working in conjunction with asecond processor configured to carry out recitations B and C.

Language of degree used herein, such as the terms “about,”“approximately,” “generally,” “nearly” or “substantially” as usedherein, represent a value, amount, or characteristic close to the statedvalue, amount, or characteristic that still performs a desired functionor achieves a desired result. For example, the terms “about,”“approximately,” “generally,” “nearly” or “substantially” may refer toan amount that is within less than 10% of, within less than 5% of,within less than 1% of, within less than 0.1% of, and within less than0.01% of the stated amount.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey in apermissive manner that certain implementations could include, or havethe potential to include, but do not mandate or require, certainfeatures, elements and/or steps. In a similar manner, terms such as“include,” “including” and “includes” are generally intended to mean“including, but not limited to.” Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more implementations or that one or moreimplementations necessarily include logic for deciding, with or withoutuser input or prompting, whether these features, elements and/or stepsare included or are to be performed in any particular implementation.

Although the invention has been described and illustrated with respectto illustrative implementations thereof, the foregoing and various otheradditions and omissions may be made therein and thereto withoutdeparting from the spirit and scope of the present disclosure.

1. A computer-implemented method, comprising: obtaining a firstplurality of content items; for each respective content item of thefirst plurality of content items: obtaining an embedding vectorrepresentative of the respective content item; processing, using atrained machine learning model and without performing pre-processing ofthe respective content item, the embedding vector to determine a hairpattern for the respective content item; and associating the hairpattern with the respective content item; determining, based at least inpart on a query received from a client device, a second plurality ofcontent items from the first plurality of content items, wherein thesecond plurality of content items are responsive to the query; causingat least a portion of the second plurality of content items and a hairpattern filter control including a plurality of selectable hair patternsto be presented on the client device; obtaining, via an interaction withthe hair pattern filter control, selection of a first hair pattern fromthe plurality of selectable hair patterns; determining, based at leastin part on the first hair pattern, a third plurality of content items,such that each of the third plurality of content items is associatedwith the first hair pattern; and causing at least a portion of the thirdplurality of content items to be presented on the client device.
 2. Thecomputer-implemented method of claim 1, wherein the hair patternincludes at least one of a protective hair pattern, a coily hairpattern, a curly hair pattern, a wavy hair pattern, a straight hairpattern, or a bald/shaved hair pattern.
 3. The computer-implementedmethod of claim 1, wherein at least the portion of the second pluralityof content items is presented on the client device according to adiversity associated with the second plurality of content items.
 4. Thecomputer-implemented method of claim 1, wherein at least the portion ofthe third plurality of content items is presented on the client deviceaccording to a diversity associated with the third plurality of contentitems.
 5. The computer-implemented method of claim 1, wherein:processing the embedding vector to determine the hair pattern for therespective content item includes determining a plurality of hairpatterns; and associating the hair pattern with the respective contentitem includes associating the plurality of hair patterns with therespective content item.
 6. The computer-implemented method of claim 1,further comprising: obtaining, via a second interaction with the hairpattern filter control, a second hair pattern from the plurality ofselectable hair patterns, wherein determining the third plurality ofcontent items from the second plurality of content items is furtherbased on the second hair pattern, such that each of the third pluralityof content items is associated with at least one of the first hairpattern or the second hair pattern.
 7. A computing system, comprising:one or more processors; a memory storing program instructions that, whenexecuted by the one or more processors, cause the one or more processorsto at least: obtain a first plurality of content items, each of thefirst plurality of content items being associated with a respective hairpattern, wherein each respective hair pattern was determined by atrained machine learning model without pre-processing of each of thefirst plurality of content items; determine a second plurality ofcontent items from the first plurality of content items that areresponsive to a query received from a client device; determine, based atleast in part on the query, that the query triggers hair patternfiltering of the second plurality of content items; in response to thedetermination that the query triggers hair pattern filtering of thesecond plurality of content items, cause a hair pattern filter controlto be presented on the client device, wherein the hair pattern filtercontrol presents a plurality of selectable hair patterns; obtain aninteraction with the hair pattern filter control selecting a first hairpattern from the plurality of selectable hair patterns; and cause atleast a portion of a third plurality of content items to be presented onthe client device, wherein each of the third plurality of content itemsis associated with the first hair pattern.
 8. The computing system ofclaim 7, wherein determining that the query triggers hair patternfiltering further includes, at least: determining that an inventory ofcontent items of the second plurality of content items associated withat least one of the plurality of selectable hair patterns exceeds athreshold.
 9. The computing system of claim 7, wherein the programinstructions, that when executed by the one or more processors, furthercause the one or more processors to at least: determine a fourthplurality of content items from the first plurality of content itemsthat are responsive to a second query; determine that an inventory ofcontent items of the fourth plurality of content items associated withat least one selectable hair pattern from the plurality of selectablehair patterns does not exceed a threshold; and determine, based at leastin part on the determination that the inventory of content items of thefourth plurality of content items associated with each selectable hairpattern from the plurality of selectable hair patterns does not exceedthe threshold, that the second query does not trigger hair patternfiltering of the fourth plurality of content items.
 10. The computingsystem of claim 7, wherein each respective hair pattern includes atleast one of a protective hair pattern, a coily hair pattern, a curlyhair pattern, a wavy hair pattern, a straight hair pattern, or abald/shaved hair pattern.
 11. The computing system of claim 7, whereinthe program instructions, that when executed by the one or moreprocessors, further cause the one or more processors to at least:determine a diversification component associated with the secondplurality of content items; and cause at least a portion of the secondplurality of content items to be presented on the client device in anarrangement based at least in part on the diversification component. 12.The computing system of claim 7, wherein presentation of at least theportion of the third plurality of content items is based at least inpart on a diversity of the third plurality of content items.
 13. Thecomputing system of claim 7, wherein each respective hair patternassociated with each of the first plurality of content items is aprimary hair pattern presented in each of the first plurality of contentitems.
 14. The computing system of claim 7, wherein each of the firstplurality of content items is associated with more than one respectivehair pattern.
 15. The computing system of claim 7, wherein determinationof each respective hair pattern includes: processing of an embeddingvector representative of each respective content item of the firstplurality of content items by the trained machine learning model,wherein the embedding vector includes a binary representation of eachrespective content item of the first plurality of content items.
 16. Acomputer-implemented method, comprising: training a machine learningmodel to determine at least one hair pattern presented in a content itemwithout performing preprocessing on the content item, wherein the atleast one hair pattern includes at least one of a protective hairpattern, a coily hair pattern, a curly hair pattern, a wavy hairpattern, a straight hair pattern, or a bald/shaved hair pattern;processing, using the trained machine learning model, a plurality ofembedding vectors associated with a plurality of content items todetermine a presented hair pattern for each of the plurality of contentitems, wherein each of the plurality of embedding vectors isrepresentative of a corresponding one of the plurality of content items;determining, based at least in part on a query received from a clientdevice, a second plurality of content items from the first plurality ofcontent items, wherein the second plurality of content items areresponsive to the query; causing a hair pattern filter control to bepresented on a client device; obtaining, via an interaction with thehair pattern filter control, selection of a first hair pattern;determining, based at least in part on the first hair pattern, a thirdplurality of content items, such that each of the third plurality ofcontent items is associated with the first hair pattern; and causing atleast a portion of the third plurality of content items to be presentedon the client device.
 17. The computer-implemented method of claim 16,further comprising: determining, based at least in part on the query,that the query triggers hair pattern filtering of the second pluralityof content items.
 18. The computer-implemented method of claim 16,further comprising: determining a hair pattern inventory of the secondplurality of content items; and determining, based at least in part onthe hair pattern inventory, a plurality of selectable hair patternoptions to be included in the hair pattern filter control that ispresented on the client device.
 19. The computer-implemented method ofclaim 16, wherein presentation of at least the portion of the thirdplurality of content items is based at least in part on a diversity ofthe third plurality of content items.
 20. The computer-implementedmethod of claim 16, further comprising: providing the machine learningmodel with a plurality of labeled content items as a training dataset totrain the machine learning model, wherein a label associated with eachof the plurality of labeled content items is determined based on aplurality of labels.